Krishna Sai Vootla, Developer in Bengaluru, Karnataka, India
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Krishna Sai Vootla

Verified Expert  in Engineering

Machine Learning Developer

Location
Bengaluru, Karnataka, India
Toptal Member Since
October 31, 2019

Krishna is a machine learning engineer who is curious and passionate about applied deep learning in computer vision, NLP, and reinforcement learning. He has four years of experience with machine learning, including being a part of the analytics division of JP Morgan Chase & Co. Krishna is a great communicator and enthusiastic developer.

Artificial Intelligence (AI)Code ReviewMachine LearningLarge Language Models (LLMs)PythonPython 3SQLAlgorithmsDeep Neural NetworksDeep LearningData AnalyticsData ReportingExploratory Data AnalysisStatistical Data AnalysisStatistical LearningRStudio ShinyDplyrVector Database

Portfolio

Organifi
Python 3, Natural Language Processing (NLP), GPT...
Multithread
Python 3, Flask, Neo4j, OpenAI GPT-4 API
JP Morgan Chase & Co
Tableau, Python, R, Code Review, Source Code Review, SQL, Machine Learning...

Experience

Python - 6 yearsMachine Learning - 4 yearsDeep Learning - 3 yearsNatural Language Processing (NLP) - 2 yearsAI Chatbots - 1 yearLangChain - 1 yearLarge Language Models (LLMs) - 1 yearOpenAI - 1 year

Availability

Full-time

Preferred Environment

PyCharm, Tableau, RStudio, Spyder, Jupyter Notebook, SQL, Machine Learning, Python

The most amazing...

...achievement of mine is winning 3rd prize globally in the Intel ESDC competition held in Shanghai, China.

Work Experience

Data Scientist

2020 - 2024
Organifi
  • Developed and deployed an NLP pipeline to extract and summarize opinions and feedback from customer product reviews.
  • Built data pipelines in AWS and GCP for a reporting and analytics data warehouse.
  • Built numerous executive summary dashboards in Tableau by identifying key metrics to track goals specific to individual teams.
  • Forecasted time series eCommerce demand for inventory and supply chain optimization using SARIMAX.
Technologies: Python 3, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Sentiment Analysis, Data Analytics, BigQuery, Google Cloud Functions, Google Cloud, Tableau, Tableau Desktop Pro, MySQL, Amazon Web Services (AWS), AWS Lambda, Amazon RDS, Data Science, Technical Hiring, Code Review, Interviewing, Source Code Review, SQL, Machine Learning, Python, Deep Learning, Dplyr, AWS Glue, Data Modeling, Snowflake, Databases, Artificial Intelligence (AI), Jupyter, Scikit-learn, Anaconda, Team Leadership

Back-end Data Engineer

2023 - 2023
Multithread
  • Developed an end-to-end ETL pipeline from scratch to load data into a graph database and relational database.
  • Built back-end APIs to create customized email text for reaching out to leads using OpenAI API.
  • Created back-end APIs to manipulate and manage data in the graph database.
Technologies: Python 3, Flask, Neo4j, OpenAI GPT-4 API

Analyst

2019 - 2019
JP Morgan Chase & Co
  • Designed and built a next-generation merchant acquisition tool in R Shiny for a credit card business.
  • Provided pricing analysis of credit card business.
  • Built and integrated a minimum revenue model based on customer demographics.
Technologies: Tableau, Python, R, Code Review, Source Code Review, SQL, Machine Learning, Jupyter, Scikit-learn, Artificial Intelligence (AI), Algorithms

Business Analyst

2018 - 2019
Tredence Analytics
  • Segmented retail customers based on their shopping behavior by using random forest.
  • Designed, built, and deployed an end-to-end machine learning pipeline.
  • Performed marketing analysis of a leading retail company in the US.
Technologies: Tableau, R, Python, Interviewing, Source Code Review, Task Analysis, Data Science, SQL, Machine Learning, Jupyter, Anaconda, Artificial Intelligence (AI)

Software Analyst

2017 - 2018
Capgemini
  • Scraped the web for collecting unstructured data present on a website.
  • Created and deployed various executive summary dashboards.
  • Automated data cleaning pipelines to save significant person-hours every week.
Technologies: Python, MySQL, Linux, Task Analysis

LLM Chatbot for Interacting with Documents

https://chat-with-docs-by-krishna-vootla.streamlit.app/
This advanced streamline-based application leverages cutting-edge generative AI technology to provide an immersive, interactive chat experience with PDF documents. Users can upload their PDFs, and the application intelligently analyzes the content, enabling various interactive features that make the information more accessible and engaging.

Users can use this application to achieve the following:
1. AI-powered summarization
2. Interactive Q&A

GitHub Link: https://github.com/krishnasaivootla/ChatWithDocs/blob/main/streamlit_app.py

AnalyticsGPT: LLM-based Data Analysis

https://analytics-gpt-by-krishna-vootla.streamlit.app/
AnalyticsGPT is a cutting-edge data analysis tool powered by a state-of-the-art language learning model (LLM). Designed to transform the way businesses, researchers, and data enthusiasts interact with their datasets, AnalyticsGPT leverages the advanced capabilities of generative AI to provide deep insights, automate data interpretation, and facilitate an intuitive analysis experience.

At the core of AnalyticsGPT is its LLM, which understands and processes complex data patterns and trends. Users can interact with their data through natural language queries, making data analysis more accessible and less time-consuming. Whether you're looking to identify key trends, predict future patterns, or simply explore your data, AnalyticsGPT offers a user-friendly platform that caters to both seasoned analysts and those new to data science.

GitHub Link: https://github.com/krishnasaivootla/AnalyticsGPT/tree/main

Multi-modal Fully Convolutional Network for Semantic Segmentation

https://github.com/prml615/prml
A fully convolutional network (FCN-32s) trained to semantically segment forest scene images with RGB and nir_color input images.

The project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes.

I used Intel Edison and Microsoft Kinect for proof of concept and prototype creation.

Smart Medical Network

I worked on a smart medical network for Intel ESDC 2016, Shanghai. The project aimed to create an ecosystem of a medical network that stores the clinical and real-time data of patients for smoother and quicker diagnosis in an emergency.

Languages

SQL, Python, Python 3, R, Snowflake, C++, C, Embedded C

Paradigms

Data Science

Other

Freelancing, Machine Learning, Artificial Intelligence (AI), Technical Hiring, Code Review, Source Code Review, Large Language Models (LLMs), Algorithms, Neural Networks, Deep Neural Networks, Deep Learning, Computer Vision, Natural Language Processing (NLP), Data Analytics, Data Reporting, Exploratory Data Analysis, Statistical Data Analysis, Statistical Learning, Statistical Modeling, Analytics, Predictive Analytics, Statistical Analysis, Data Analysis, Artificial Neural Networks (ANN), Interviewing, Task Analysis, GPT, Generative Pre-trained Transformers (GPT), Chatbots, Minimum Viable Product (MVP), APIs, Data Modeling, Open-source LLMs, Team Leadership, Quantitative Analysis, Sentiment Analysis, Google Cloud Functions, Amazon RDS, Generative AI, LangChain, Scalable Vector Databases, AI Chatbots, ChatGPT, Chatbot Conversation Design, Generative Artificial Intelligence (GenAI), OpenAI, OpenAI GPT-4 API

Frameworks

RStudio Shiny, Microsoft Kinect, Flask

Libraries/APIs

Keras, NumPy, Pandas, Matplotlib, Ggplot2, Scikit-learn, Tidyverse, Beautiful Soup, Standard Template Library (STL), SciPy, OpenCV, TensorFlow

Tools

Tableau, Dplyr, Scikit-image, Looker, AWS Glue, PyCharm, Jupyter, Spyder, BigQuery, Tableau Desktop Pro

Platforms

Amazon Web Services (AWS), RStudio, Linux, Oracle, Arduino, Raspberry Pi, Raspberry Pi 3 GPIO, Jupyter Notebook, Anaconda, AWS Lambda

Storage

MySQL, Databases, Google Cloud, Google Cloud Storage, Neo4j

2013 - 2017

Bachelor of Technology Degree in Electrical Engineering

Indian Institute of Technology Gandhinagar - Gandhinagar, India

MARCH 2020 - PRESENT

Statistical Learning

Stanford Online

OCTOBER 2019 - PRESENT

Sentiment Analysis in Python

DataCamp

MARCH 2019 - PRESENT

Building Web Applications in R with Shiny: Case Studies

DataCamp

MARCH 2019 - PRESENT

Building Web Applications in R with Shiny

DataCamp

OCTOBER 2018 - PRESENT

CodeChef Certified Data Structure & Algorithms Programme

CodeChef

SEPTEMBER 2018 - PRESENT

Intermediate R

DataCamp

AUGUST 2018 - PRESENT

Data Manipulation in R with dplyr

DataCamp

JULY 2018 - PRESENT

Introduction to R

DataCamp

MARCH 2018 - PRESENT

Python A-Z: Python for Data Science with Real Exercises!

Udemy

MARCH 2018 - PRESENT

SQL - MySQL for Data Analytics & Business Intelligence

Udemy

FEBRUARY 2018 - PRESENT

Structuring Machine Learning Projects

Coursera

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